Overview

Dataset statistics

Number of variables15
Number of observations243
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.6 KiB
Average record size in memory120.5 B

Variable types

Numeric11
Categorical4

Alerts

year has constant value "2012"Constant
BUI is highly overall correlated with Classes and 8 other fieldsHigh correlation
Classes is highly overall correlated with BUI and 6 other fieldsHigh correlation
DC is highly overall correlated with BUI and 7 other fieldsHigh correlation
DMC is highly overall correlated with BUI and 9 other fieldsHigh correlation
FFMC is highly overall correlated with BUI and 8 other fieldsHigh correlation
FWI is highly overall correlated with BUI and 8 other fieldsHigh correlation
ISI is highly overall correlated with BUI and 8 other fieldsHigh correlation
RH is highly overall correlated with DMC and 4 other fieldsHigh correlation
Rain is highly overall correlated with BUI and 5 other fieldsHigh correlation
Temperature is highly overall correlated with BUI and 7 other fieldsHigh correlation
day is highly overall correlated with BUI and 1 other fieldsHigh correlation
Rain has 133 (54.7%) zerosZeros
ISI has 4 (1.6%) zerosZeros
FWI has 9 (3.7%) zerosZeros

Reproduction

Analysis started2026-01-25 12:29:25.218614
Analysis finished2026-01-25 12:29:41.647217
Duration16.43 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

day
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.761317
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2026-01-25T17:59:41.745930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8425522
Coefficient of variation (CV)0.56102877
Kurtosis-1.2056
Mean15.761317
Median Absolute Deviation (MAD)8
Skewness0.00036459881
Sum3830
Variance78.190729
MonotonicityNot monotonic
2026-01-25T17:59:41.881796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
18
 
3.3%
28
 
3.3%
38
 
3.3%
48
 
3.3%
58
 
3.3%
68
 
3.3%
78
 
3.3%
88
 
3.3%
98
 
3.3%
108
 
3.3%
Other values (21)163
67.1%
ValueCountFrequency (%)
18
3.3%
28
3.3%
38
3.3%
48
3.3%
58
3.3%
68
3.3%
78
3.3%
88
3.3%
98
3.3%
108
3.3%
ValueCountFrequency (%)
314
1.6%
308
3.3%
298
3.3%
288
3.3%
278
3.3%
268
3.3%
258
3.3%
248
3.3%
238
3.3%
228
3.3%

month
Categorical

Distinct4
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
8
62 
7
61 
6
60 
9
60 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters243
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
862
25.5%
761
25.1%
660
24.7%
960
24.7%

Length

2026-01-25T17:59:42.032954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-25T17:59:42.145310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
862
25.5%
761
25.1%
660
24.7%
960
24.7%

Most occurring characters

ValueCountFrequency (%)
862
25.5%
761
25.1%
660
24.7%
960
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
862
25.5%
761
25.1%
660
24.7%
960
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
862
25.5%
761
25.1%
660
24.7%
960
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
862
25.5%
761
25.1%
660
24.7%
960
24.7%

year
Categorical

Constant 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2012
243 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters972
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2012
2nd row2012
3rd row2012
4th row2012
5th row2012

Common Values

ValueCountFrequency (%)
2012243
100.0%

Length

2026-01-25T17:59:42.288760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-25T17:59:42.372050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2012243
100.0%

Most occurring characters

ValueCountFrequency (%)
2486
50.0%
0243
25.0%
1243
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2486
50.0%
0243
25.0%
1243
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2486
50.0%
0243
25.0%
1243
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2486
50.0%
0243
25.0%
1243
25.0%

Temperature
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.152263
Minimum22
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2026-01-25T17:59:42.469057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26
Q130
median32
Q335
95-th percentile37.9
Maximum42
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6280395
Coefficient of variation (CV)0.11283932
Kurtosis-0.14141446
Mean32.152263
Median Absolute Deviation (MAD)3
Skewness-0.19132733
Sum7813
Variance13.16267
MonotonicityNot monotonic
2026-01-25T17:59:42.608746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3529
11.9%
3125
10.3%
3424
9.9%
3323
9.5%
3022
9.1%
3621
8.6%
3221
8.6%
2918
7.4%
2815
6.2%
278
 
3.3%
Other values (9)37
15.2%
ValueCountFrequency (%)
222
 
0.8%
243
 
1.2%
256
 
2.5%
265
 
2.1%
278
 
3.3%
2815
6.2%
2918
7.4%
3022
9.1%
3125
10.3%
3221
8.6%
ValueCountFrequency (%)
421
 
0.4%
403
 
1.2%
396
 
2.5%
383
 
1.2%
378
 
3.3%
3621
8.6%
3529
11.9%
3424
9.9%
3323
9.5%
3221
8.6%

RH
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.041152
Minimum21
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2026-01-25T17:59:42.851806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile37
Q152.5
median63
Q373.5
95-th percentile86
Maximum90
Range69
Interquartile range (IQR)21

Descriptive statistics

Standard deviation14.82816
Coefficient of variation (CV)0.23900523
Kurtosis-0.50894281
Mean62.041152
Median Absolute Deviation (MAD)11
Skewness-0.24279046
Sum15076
Variance219.87433
MonotonicityNot monotonic
2026-01-25T17:59:43.062857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6410
 
4.1%
5510
 
4.1%
788
 
3.3%
548
 
3.3%
588
 
3.3%
737
 
2.9%
807
 
2.9%
667
 
2.9%
687
 
2.9%
657
 
2.9%
Other values (52)164
67.5%
ValueCountFrequency (%)
211
 
0.4%
241
 
0.4%
261
 
0.4%
291
 
0.4%
311
 
0.4%
332
0.8%
343
1.2%
351
 
0.4%
361
 
0.4%
373
1.2%
ValueCountFrequency (%)
901
 
0.4%
893
1.2%
883
1.2%
874
1.6%
863
1.2%
842
 
0.8%
831
 
0.4%
823
1.2%
816
2.5%
807
2.9%

Ws
Real number (ℝ)

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.493827
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2026-01-25T17:59:43.204445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q114
median15
Q317
95-th percentile20
Maximum29
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8113853
Coefficient of variation (CV)0.18145196
Kurtosis2.6217035
Mean15.493827
Median Absolute Deviation (MAD)2
Skewness0.55558584
Sum3765
Variance7.9038874
MonotonicityNot monotonic
2026-01-25T17:59:43.370747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1443
17.7%
1540
16.5%
1330
12.3%
1728
11.5%
1627
11.1%
1825
10.3%
1915
 
6.2%
218
 
3.3%
117
 
2.9%
127
 
2.9%
Other values (8)13
 
5.3%
ValueCountFrequency (%)
61
 
0.4%
81
 
0.4%
92
 
0.8%
103
 
1.2%
117
 
2.9%
127
 
2.9%
1330
12.3%
1443
17.7%
1540
16.5%
1627
11.1%
ValueCountFrequency (%)
291
 
0.4%
261
 
0.4%
222
 
0.8%
218
 
3.3%
202
 
0.8%
1915
 
6.2%
1825
10.3%
1728
11.5%
1627
11.1%
1540
16.5%

Rain
Real number (ℝ)

High correlation  Zeros 

Distinct39
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76296296
Minimum0
Maximum16.8
Zeros133
Zeros (%)54.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2026-01-25T17:59:43.511415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.5
95-th percentile4.37
Maximum16.8
Range16.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation2.0032068
Coefficient of variation (CV)2.6255623
Kurtosis25.822987
Mean0.76296296
Median Absolute Deviation (MAD)0
Skewness4.5686298
Sum185.4
Variance4.0128375
MonotonicityNot monotonic
2026-01-25T17:59:43.714632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0133
54.7%
0.118
 
7.4%
0.211
 
4.5%
0.310
 
4.1%
0.48
 
3.3%
0.76
 
2.5%
0.66
 
2.5%
0.55
 
2.1%
1.83
 
1.2%
1.13
 
1.2%
Other values (29)40
 
16.5%
ValueCountFrequency (%)
0133
54.7%
0.118
 
7.4%
0.211
 
4.5%
0.310
 
4.1%
0.48
 
3.3%
0.55
 
2.1%
0.66
 
2.5%
0.76
 
2.5%
0.82
 
0.8%
0.91
 
0.4%
ValueCountFrequency (%)
16.81
0.4%
13.11
0.4%
10.11
0.4%
8.71
0.4%
8.31
0.4%
7.21
0.4%
6.51
0.4%
61
0.4%
5.81
0.4%
4.71
0.4%

FFMC
Real number (ℝ)

High correlation 

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.842387
Minimum28.6
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2026-01-25T17:59:43.947738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum28.6
5-th percentile47.13
Q171.85
median83.3
Q388.3
95-th percentile92.19
Maximum96
Range67.4
Interquartile range (IQR)16.45

Descriptive statistics

Standard deviation14.349641
Coefficient of variation (CV)0.18434226
Kurtosis1.040087
Mean77.842387
Median Absolute Deviation (MAD)5.8
Skewness-1.3201301
Sum18915.7
Variance205.9122
MonotonicityNot monotonic
2026-01-25T17:59:44.091621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.97
 
2.9%
89.45
 
2.1%
89.34
 
1.6%
85.44
 
1.6%
89.14
 
1.6%
88.13
 
1.2%
78.33
 
1.2%
47.43
 
1.2%
88.33
 
1.2%
79.93
 
1.2%
Other values (163)204
84.0%
ValueCountFrequency (%)
28.61
0.4%
30.51
0.4%
36.11
0.4%
37.31
0.4%
37.91
0.4%
40.91
0.4%
41.11
0.4%
42.61
0.4%
44.91
0.4%
451
0.4%
ValueCountFrequency (%)
961
0.4%
94.31
0.4%
94.21
0.4%
93.92
0.8%
93.81
0.4%
93.71
0.4%
93.31
0.4%
931
0.4%
92.52
0.8%
92.22
0.8%

DMC
Real number (ℝ)

High correlation 

Distinct165
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.680658
Minimum0.7
Maximum65.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2026-01-25T17:59:44.298743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.9
Q15.8
median11.3
Q320.8
95-th percentile41.04
Maximum65.9
Range65.2
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.39304
Coefficient of variation (CV)0.84417465
Kurtosis2.462551
Mean14.680658
Median Absolute Deviation (MAD)6.9
Skewness1.5229829
Sum3567.4
Variance153.58743
MonotonicityNot monotonic
2026-01-25T17:59:44.448113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95
 
2.1%
12.54
 
1.6%
1.94
 
1.6%
2.53
 
1.2%
3.43
 
1.2%
2.63
 
1.2%
33
 
1.2%
1.33
 
1.2%
5.83
 
1.2%
4.63
 
1.2%
Other values (155)209
86.0%
ValueCountFrequency (%)
0.71
 
0.4%
0.92
0.8%
1.12
0.8%
1.21
 
0.4%
1.33
1.2%
1.71
 
0.4%
1.94
1.6%
2.11
 
0.4%
2.22
0.8%
2.41
 
0.4%
ValueCountFrequency (%)
65.91
0.4%
61.31
0.4%
56.31
0.4%
54.21
0.4%
51.31
0.4%
50.21
0.4%
471
0.4%
46.61
0.4%
46.11
0.4%
45.61
0.4%

DC
Real number (ℝ)

High correlation 

Distinct197
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.430864
Minimum6.9
Maximum220.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2026-01-25T17:59:44.613313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile7.6
Q112.35
median33.1
Q369.1
95-th percentile158.94
Maximum220.4
Range213.5
Interquartile range (IQR)56.75

Descriptive statistics

Standard deviation47.665606
Coefficient of variation (CV)0.96428834
Kurtosis1.5964668
Mean49.430864
Median Absolute Deviation (MAD)23.9
Skewness1.4734602
Sum12011.7
Variance2272.01
MonotonicityNot monotonic
2026-01-25T17:59:44.827672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85
 
2.1%
7.84
 
1.6%
8.34
 
1.6%
7.64
 
1.6%
8.44
 
1.6%
7.54
 
1.6%
8.24
 
1.6%
173
 
1.2%
102
 
0.8%
7.42
 
0.8%
Other values (187)207
85.2%
ValueCountFrequency (%)
6.91
 
0.4%
72
0.8%
7.11
 
0.4%
7.32
0.8%
7.42
0.8%
7.54
1.6%
7.64
1.6%
7.72
0.8%
7.84
1.6%
7.91
 
0.4%
ValueCountFrequency (%)
220.41
0.4%
210.41
0.4%
200.21
0.4%
190.61
0.4%
181.31
0.4%
180.41
0.4%
177.31
0.4%
171.31
0.4%
168.21
0.4%
167.21
0.4%

ISI
Real number (ℝ)

High correlation  Zeros 

Distinct106
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7423868
Minimum0
Maximum19
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2026-01-25T17:59:45.071182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.4
median3.5
Q37.25
95-th percentile13.38
Maximum19
Range19
Interquartile range (IQR)5.85

Descriptive statistics

Standard deviation4.1542338
Coefficient of variation (CV)0.87597954
Kurtosis0.86232522
Mean4.7423868
Median Absolute Deviation (MAD)2.4
Skewness1.1402426
Sum1152.4
Variance17.257659
MonotonicityNot monotonic
2026-01-25T17:59:45.205291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.18
 
3.3%
1.27
 
2.9%
0.45
 
2.1%
5.65
 
2.1%
4.75
 
2.1%
5.25
 
2.1%
2.85
 
2.1%
1.55
 
2.1%
15
 
2.1%
1.34
 
1.6%
Other values (96)189
77.8%
ValueCountFrequency (%)
04
1.6%
0.14
1.6%
0.24
1.6%
0.33
1.2%
0.45
2.1%
0.52
 
0.8%
0.64
1.6%
0.74
1.6%
0.83
1.2%
0.92
 
0.8%
ValueCountFrequency (%)
191
0.4%
18.51
0.4%
17.21
0.4%
16.61
0.4%
161
0.4%
15.72
0.8%
15.51
0.4%
14.31
0.4%
14.21
0.4%
13.82
0.8%

BUI
Real number (ℝ)

High correlation 

Distinct173
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.690535
Minimum1.1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2026-01-25T17:59:45.445881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.42
Q16
median12.4
Q322.65
95-th percentile46.4
Maximum68
Range66.9
Interquartile range (IQR)16.65

Descriptive statistics

Standard deviation14.228421
Coefficient of variation (CV)0.85248443
Kurtosis1.9560166
Mean16.690535
Median Absolute Deviation (MAD)7.3
Skewness1.4527448
Sum4055.8
Variance202.44797
MonotonicityNot monotonic
2026-01-25T17:59:45.669191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35
 
2.1%
5.14
 
1.6%
10.93
 
1.2%
4.43
 
1.2%
3.93
 
1.2%
11.53
 
1.2%
2.93
 
1.2%
8.33
 
1.2%
14.23
 
1.2%
2.43
 
1.2%
Other values (163)210
86.4%
ValueCountFrequency (%)
1.11
 
0.4%
1.42
0.8%
1.62
0.8%
1.72
0.8%
1.82
0.8%
2.21
 
0.4%
2.43
1.2%
2.62
0.8%
2.72
0.8%
2.82
0.8%
ValueCountFrequency (%)
681
0.4%
67.41
0.4%
641
0.4%
62.91
0.4%
59.51
0.4%
59.31
0.4%
57.11
0.4%
54.91
0.4%
54.71
0.4%
50.91
0.4%

FWI
Real number (ℝ)

High correlation  Zeros 

Distinct125
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0353909
Minimum0
Maximum31.1
Zeros9
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2026-01-25T17:59:45.820094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median4.2
Q311.45
95-th percentile21.53
Maximum31.1
Range31.1
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation7.4405677
Coefficient of variation (CV)1.0575912
Kurtosis0.65498526
Mean7.0353909
Median Absolute Deviation (MAD)3.8
Skewness1.1475925
Sum1709.6
Variance55.362048
MonotonicityNot monotonic
2026-01-25T17:59:46.026652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.412
 
4.9%
0.810
 
4.1%
0.59
 
3.7%
09
 
3.7%
0.19
 
3.7%
0.38
 
3.3%
0.97
 
2.9%
0.26
 
2.5%
0.75
 
2.1%
0.64
 
1.6%
Other values (115)164
67.5%
ValueCountFrequency (%)
09
3.7%
0.19
3.7%
0.26
2.5%
0.38
3.3%
0.412
4.9%
0.59
3.7%
0.64
 
1.6%
0.75
2.1%
0.810
4.1%
0.97
2.9%
ValueCountFrequency (%)
31.11
0.4%
30.31
0.4%
30.21
0.4%
301
0.4%
26.91
0.4%
26.31
0.4%
26.11
0.4%
25.41
0.4%
24.51
0.4%
241
0.4%

Classes
Categorical

High correlation 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
fire
137 
not fire
106 

Length

Max length8
Median length4
Mean length5.744856
Min length4

Characters and Unicode

Total characters1396
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot fire
2nd rownot fire
3rd rownot fire
4th rownot fire
5th rownot fire

Common Values

ValueCountFrequency (%)
fire137
56.4%
not fire106
43.6%

Length

2026-01-25T17:59:46.214285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-25T17:59:46.290294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fire243
69.6%
not106
30.4%

Most occurring characters

ValueCountFrequency (%)
f243
17.4%
i243
17.4%
r243
17.4%
e243
17.4%
n106
7.6%
o106
7.6%
t106
7.6%
106
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f243
17.4%
i243
17.4%
r243
17.4%
e243
17.4%
n106
7.6%
o106
7.6%
t106
7.6%
106
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f243
17.4%
i243
17.4%
r243
17.4%
e243
17.4%
n106
7.6%
o106
7.6%
t106
7.6%
106
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f243
17.4%
i243
17.4%
r243
17.4%
e243
17.4%
n106
7.6%
o106
7.6%
t106
7.6%
106
7.6%

region
Categorical

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
122 
1
121 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters243
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0122
50.2%
1121
49.8%

Length

2026-01-25T17:59:46.376635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-25T17:59:46.450731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0122
50.2%
1121
49.8%

Most occurring characters

ValueCountFrequency (%)
0122
50.2%
1121
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)243
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0122
50.2%
1121
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)243
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0122
50.2%
1121
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)243
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0122
50.2%
1121
49.8%

Interactions

2026-01-25T17:59:38.658810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:25.864089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:27.236108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:28.693481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:30.184522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:31.191366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:32.236494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:33.305039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:34.759417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:36.033787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:37.284282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:39.962214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:25.966055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:27.331827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:28.849580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:30.257303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:31.277187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:32.323355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:33.446485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:34.865712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:36.161763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:37.429474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:40.112632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:26.089916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:27.470726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:29.060562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:30.386925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:31.376618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:32.416041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:33.576275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:35.029212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:36.272082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:37.539908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:40.256701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:26.211326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:27.660217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:29.216127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:30.496956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:31.463373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:32.497893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:33.695655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:35.158121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:36.359738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:37.649093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:40.378411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:26.392174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:27.837729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:29.355088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:30.582668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:31.549178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:32.575406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:33.843440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:35.289529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:36.485780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:37.757027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:40.513906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:26.531961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:27.956818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:29.469165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:30.724421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:31.639464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:32.659975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:33.987704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:35.409953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:36.607034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:37.875049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:40.639105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:26.656324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:28.052509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:29.594829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:30.797114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:31.746406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:32.737435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:34.088538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:35.495049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:36.695878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:38.003088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:40.750722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:26.756576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:28.173645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:29.711253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:30.872481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:31.854815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:32.817593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:34.231149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:35.563246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:36.790457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:38.121273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:40.844155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:26.864120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:28.274829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:29.833163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:30.942035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:31.952709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:32.896909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:34.387608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:35.623538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:36.869953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:38.250598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:40.966851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:26.999448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:28.407468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:29.944767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:31.021707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:32.053580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:33.004519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:34.537892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:35.742376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:36.979136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:38.380256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:41.108668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:27.143268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:28.536143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:30.055523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:31.105897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:32.148955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:33.131401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:34.636472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:35.908677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:37.086693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-25T17:59:38.465889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-25T17:59:46.522242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BUIClassesDCDMCFFMCFWIISIRHRainTemperatureWsdaymonthregion
BUI1.0000.7260.9430.9880.8070.9110.811-0.467-0.5760.5860.0270.5170.3260.000
Classes0.7261.0000.6210.6930.9110.8610.8820.4070.3470.5000.1370.2310.3400.140
DC0.9430.6211.0000.8930.7350.8490.746-0.347-0.6120.5050.0600.4800.2810.080
DMC0.9880.6930.8931.0000.8220.9160.822-0.505-0.5590.6110.0010.5030.3300.115
FFMC0.8070.9110.7350.8221.0000.9680.989-0.665-0.7410.666-0.0670.2510.2600.213
FWI0.9110.8610.8490.9160.9681.0000.975-0.598-0.7180.6570.0340.3470.2710.092
ISI0.8110.8820.7460.8220.9890.9751.000-0.643-0.7380.6480.0320.2390.2450.242
RH-0.4670.407-0.347-0.505-0.665-0.598-0.6431.0000.179-0.6430.201-0.0900.2230.421
Rain-0.5760.347-0.612-0.559-0.741-0.718-0.7380.1791.000-0.2930.011-0.1700.0890.082
Temperature0.5860.5000.5050.6110.6660.6570.648-0.643-0.2931.000-0.2240.1230.3910.316
Ws0.0270.1370.0600.001-0.0670.0340.0320.2010.011-0.2241.0000.0710.1220.262
day0.5170.2310.4800.5030.2510.3470.239-0.090-0.1700.1230.0711.0000.0000.000
month0.3260.3400.2810.3300.2600.2710.2450.2230.0890.3910.1220.0001.0000.000
region0.0000.1400.0800.1150.2130.0920.2420.4210.0820.3160.2620.0000.0001.000

Missing values

2026-01-25T17:59:41.324776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-25T17:59:41.538548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

daymonthyearTemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesregion
01620122957180.065.73.47.61.33.40.5not fire0
12620122961131.364.44.17.61.03.90.4not fire0
236201226822213.147.12.57.10.32.70.1not fire0
34620122589132.528.61.36.90.01.70.0not fire0
45620122777160.064.83.014.21.23.90.5not fire0
56620123167140.082.65.822.23.17.02.5fire0
67620123354130.088.29.930.56.410.97.2fire0
78620123073150.086.612.138.35.613.57.1fire0
89620122588130.252.97.938.80.410.50.3not fire0
910620122879120.073.29.546.31.312.60.9not fire0
daymonthyearTemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesregion
23321920123534170.092.223.697.313.829.421.6fire1
23422920123364130.088.926.1106.37.132.413.7fire1
23523920123556140.089.029.4115.67.536.015.2fire1
2362492012264962.061.311.928.10.611.90.4not fire1
23725920122870150.079.913.836.12.414.13.0not fire1
23826920123065140.085.416.044.54.516.96.5fire1
23927920122887154.441.16.58.00.16.20.0not fire1
24028920122787290.545.93.57.90.43.40.2not fire1
24129920122454180.179.74.315.21.75.10.7not fire1
24230920122464150.267.33.816.51.24.80.5not fire1